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easemlclient-0.1.8
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مشاهده بیشتر
تبلیغات ما
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مشاهده بیشتر
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مشاهده بیشترتوضیحات
Client library used to communicate with the ease.ml service.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | easemlclient-0.1.8 |
| نام | easemlclient |
| نسخه کتابخانه | 0.1.8 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Bojan Karlas |
| ایمیل نویسنده | bojan.karlas@gmail.com |
| آدرس صفحه اصلی | https://github.com/DS3Lab/easeml |
| آدرس اینترنتی | https://pypi.org/project/easemlclient/ |
| مجوز | MIT |
# Ease.ml Client
This is the Python implementation of the ease.ml client.
## Installation
This package is available on PyPI.
```bash
pip install easemlclient
```
## Example usage
### Establishing a connection
To use the client API we first need to create a connection object that we will be using to target the running easeml instance. The connection must be inialized with a host name (here we use localhost) and either the API key or a username and password.
```python
from easemlclient.model import Connection
connection = Connection(host="localhost:8080", api_key="some-api-key")
```
### Querying Collections
Then we can query all the running jobs. To do that we need to create a `JobQuery` instance which we use to specify the parameters of our query. For example, we can query all completed jobs. To get the result we call the `run()` method of the query object and pass the connection instance.
```python
from easemlclient.model import JobQuery
query = JobQuery(status="completed")
result, next_query = query.run(connection)
```
The result will contain a list of `Job` objects taht satisfy our query. Results are paginated to limit the size of each request. If there are more pages to be loaded, then the `next_query` variable will contain a `JobQuery` instance that we can run and return the next page. The full pattern for loading all jobs is the following:
```python
from easemlclient.model import JobQuery
result, query = [], JobQuery(status="completed")
next_result, next_query = [], query
while next_query is not None:
next_result, next_query = query.run(connection)
result.extend(next_result)
```
We can take the first completed job and get a list of its tasks.
```python
job = result[0]
tasks = job.tasks
```
The `tasks` list actually contains "shallow" instances of the `Task` class. This means that each instance contains only the task's `id` field and no other fields. This is normal because the `Job` object has only references to tasks, not entire tasks. To get a full version of a task given a "shallow" instance, we use the `get()` method.
```python
task = tasks[0].get(connection)
```
### Querying Specific Objects
The `Task` object can also be used to query tasks by their ID. We simply create a new "shallow" instance using a task ID and call the `get()` method.
```python
from easemlclient.model import Task
task = Task(id="some-task-id").get(connection)
```
### Creating Objects
We have the ability to create certain objects, such as `Dataset`, `Module` and `Job`. We do this by initializing an instance of that object, assigning values to relevant fields and calling the `post()` method. Here is an example of creating a dataset object along with uploading of a dataset.
```python
from easemlclient.model import Dataset, DatasetSource, DatasetStatus
dataset = Dataset.create(id="test_dataset_1", source=DatasetSource.UPLOAD, name="Test Dataset 1").post(connection)
with open("test_dataset_1.tar", "rb") as f:
dataset.upload(connection=connection, data=f)
# Once the dataset upload finishes, we need to update the status of the dataset to "transferred".
dataset.status = DatasetStatus.TRANSFERRED
# Once we assign values to fields, we use the patch command
# to apply updates to the dataset object on the server.
dataset.patch(connection)
```
### Starting a new training Job and monitoring it
Here we show a slightly more complex example that demonstrates how to start a model selection and tuning job given a previously uploaded dataset.
We will first fetch the dataset object in order to be able to access its schema.
```python
from easemlclient.model import Dataset
dataset = Dataset(id="test_dataset_1").get(connection)
```
Then we query all models that are applicable to the given dataset. We use the `ModuleQuery` class for this.
```python
from easemlclient.model import ModuleQuery, ModuleType
query = ModuleQuery(type=ModuleType.MODEL, status=ModuleStatus.ACTIVE,
schema_in=dataset.schema_in, schema_out=dataset.schema_out)
# We assume that the result does not contain more than one page.
models, _ = query.run(connection)
```
We do the same for objectives.
```python
from easemlclient.model import ModuleQuery, ModuleType
query = ModuleQuery(type=ModuleType.OBJECTIVE, status=ModuleStatus.ACTIVE,
schema_in=dataset.schema_in, schema_out=dataset.schema_out)
objectives, _ = query.run(connection)
# We will simply pick the first objective here.
objective = objectives[0]
```
Then we are ready to create a job.
```python
from easemlclient.model import Job
job = Job(dataset=dataset, objective=objective, models=models, max_tasks=20).post(connection)
```
With `max_tasks` we specify the number of tasks to run before a job's status will become `completed`. We can keep querying the job to check the status.
```python
from time import sleep
from easemlclient.model import JobStatus
while job.get(connection).status != JobStatus.COMPLETED:
time.sleep(10)
```
Once the job is completed, we can get the task with the best result.
```python
from easemlclient.model import TaskQuery, ApiQueryOrder
tasks, _ = TaskQuery(job=job, order_by="quality", order=ApiQueryOrder.DESC).run(connection)
best_task = tasks[0].get(connection)
```
Finally, we can download the Docker image of the best task and save it as a tar file.
```python
image = best_task.get_image(connection)
open("/output/path/to/image.tar", "wb").write(image)
```
نیازمندی
| مقدار | نام |
|---|---|
| >=2.22.0 | requests |
| >=1.1 | pyrfc3339 |
| >=0.2.4 | tuspy |
| >=4.0.1 | docker |
نحوه نصب
نصب پکیج whl easemlclient-0.1.8:
pip install easemlclient-0.1.8.whl
نصب پکیج tar.gz easemlclient-0.1.8:
pip install easemlclient-0.1.8.tar.gz